4 citations · 11 across the 20 of their papers we have counts for
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Can Generic LLMs Help Analyze Child-adult Interactions Involving Children with Autism in Clinical Observation?
Tiantian Feng, Anfeng Xu, Rimita Lahiri +5
Large Language Models (LLMs) have shown significant potential in understanding human communication and interaction. However, their performance in the domain of child-inclusive inte…
Data Efficient Child-Adult Speaker Diarization with Simulated Conversations
Anfeng Xu, Tiantian Feng, Helen Tager-Flusberg +2
Automating child speech analysis is crucial for applications such as neurocognitive assessments. Speaker diarization, which identifies ``who spoke when'', is an essential component…
Examining Test-Time Adaptation for Personalized Child Speech Recognition
Zhonghao Shi, Xuan Shi, Anfeng Xu +4
Automatic speech recognition (ASR) models often experience performance degradation due to data domain shifts introduced at test time, a challenge that is further amplified for chil…
Egocentric Speaker Classification in Child-Adult Dyadic Interactions: From Sensing to Computational Modeling
Tiantian Feng, Anfeng Xu, Xuan Shi +2
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication, repetitive behavior, and sensory processing. One important res…
Effective Integration of KAN for Keyword Spotting
Anfeng Xu, Biqiao Zhang, Shuyu Kong +4
Keyword spotting (KWS) is an important speech processing component for smart devices with voice assistance capability. In this paper, we investigate if Kolmogorov-Arnold Networks (…
Exploring Speech Foundation Models for Speaker Diarization in Child-Adult Dyadic Interactions
Anfeng Xu, Kevin Huang, Tiantian Feng +3
Speech foundation models, trained on vast datasets, have opened unique opportunities in addressing challenging low-resource speech understanding, such as child speech. In this work…